Dot Pattern Ratings - data checks

• catLearn accuracy refers to accuracy on the categorization task in the Hu & Nosofsky 2024 study.
• prototype sets refer to sets of 3 prototypes that correspond to a single participant in the Hu & Nosofsky 2024 study.
• prototype pairs refer to the specific pairs of prototypes that are displayed together on a rating trial (3 pairs per set).

pacman::p_load(dplyr,purrr,tidyr,ggplot2, here, patchwork, 
  conflicted, jsonlite,stringr, gt, knitr, kableExtra, 
  lubridate,ggh4x, lmerTest)
walk(c("dplyr", "lmerTest"), conflict_prefer_all, quiet = TRUE)
options(digits=2, scipen=999, dplyr.summarise.inform=FALSE)
walk(c("fun_plot"), ~ source(here::here(paste0("R/", .x, ".R"))))
mc24_proto <- read.csv(here("Stimulii","mc24_prototypes.csv")) |> mutate(set=paste0(sbjCode,"_",condit)) 
sbj_cat <- read.csv(here("data","mc24_sbj_cat.csv"))

dfiles <- list(path=list.files(here::here("data/dotSim_data"),full.names=TRUE))

d <- map_dfr(dfiles$path, ~read.csv(.x))

d <- map_dfr(dfiles$path, ~{read.csv(.x) |> 
    mutate(sfile=tools::file_path_sans_ext(basename(.x)))}) |> 
  select(-trial_index, -internal_node_id,-trial_type) |>
   mutate(set = paste(str_extract(item_label_1, "^\\d+"),
                     str_extract(item_label_1, "[a-z]+"), sep = "_")) |>
  mutate(pair_label = paste0(item_label_1,"_",item_label_2)) |>
  relocate(sbjCode,date,set,pair_label,trial,item_label_1,item_label_2,response,rt)

setCounts <- d |> 
  pivot_longer(cols=c(item_label_1, item_label_2), names_to="item_label", values_to="item") |> 
  group_by(set) |> summarise(n=n_distinct(sbjCode),resp=mean(response),sd=sd(response)) |> arrange(desc(n))

# length(unique(mc_proto$set)) # 304
setCounts2 <- mc24_proto |> group_by(set) |> 
  slice_head(n=1) |> 
  select(id,file,set) |> 
  left_join(setCounts,by="set") |> 
  mutate(n = ifelse(is.na(n), 0, n), .groups="drop") |> 
  arrange(n) |> ungroup()

pairCounts <- d |> 
  group_by(pair_label,set) |> 
  summarise(n=n(),mean_resp=mean(response),sd=sd(response)) |> arrange(desc(n)) |> ungroup()



patternAvg <- d |> 
  pivot_longer(cols=c(item_label_1, item_label_2), names_to="item_label", values_to="item") |> 
  group_by(item,file) |> 
  summarise(n_rating=n(),resp=mean(response),sd=sd(response)) |> 
  arrange(desc(n_rating))

cat_sim <- sbj_cat |> 
  mutate(item=item_label) |> 
  left_join(patternAvg,by=c("file","item"))  |> arrange(desc(n_rating)) |>
  #remove rows where n_rating is NA, or less than 4
  filter(!is.na(n_rating),n_rating>=12) |> 
  mutate(sim_group = ifelse(resp>6.0,"Very Similar",ifelse(resp<3.5,"Very Dissimilar","Medium"))) |> 
  mutate(sim_group=factor(sim_group,levels=c("Very Dissimilar","Medium","Very Similar"))) 



cat_sim_test <- cat_sim |> 
  filter(Phase==2) 


#cor(cat_sim$resp,cat_sim$Corr)

#  m1 <- lmer(Corr ~ resp + (1|sbjCode), data=cat_sim)
#  summary(m1)

#  m1 <- lmer(Corr ~ resp + (1|Pattern.Type) +  (1|sbjCode), data=cat_sim)
#  summary(m1)

#  m1 <- lmer(Corr ~ resp*Pattern.Type*condit +  (1|sbjCode), data=cat_sim)
#  summary(m1)


# m1 <- lmer(Corr ~ sim_group +  (1|sbjCode), data=cat_sim)
# summary(m1)

# m1 <- lmer(Corr ~ sim_group*condit +  (1|sbjCode), data=cat_sim)
# summary(m1)

# m1 <- lmer(Corr ~ sim_group*condit*Pattern.Type +  (1|sbjCode), data=cat_sim)
# summary(m1)

Data Inspection & Sanity Checks

avg_set_rating <- setCounts2 |> summarise("Avg Ratings Per Set" = mean(n)) |> pull(1)

d |> 
  summarize("N Subjects" = n_distinct(sbjCode), "N Prototype Sets" = n_distinct(set)) |> 
  mutate("Avg Ratings Per Set" = avg_set_rating) |>
  kbl()

Table 1: Current counts of unique subjects, and prototype sets

N Subjects	N Prototype Sets	Avg Ratings Per Set
87	304	29

Prototype set counts

 

setCounts2 |> group_by(n) |> summarise(nc=n()) |> rename("Number of times prototype set has been included in the study"=n, "Number of prototype sets with this count"=nc) |> gt() |> 
  tab_spanner(label = "Prototype Set Counts") |> 
  tab_header(title = "Prototype Set Counts") |> 
  tab_source_note(
    "Note: The number of times a prototype set has been included in the study is equal to the number of participants who rated the set."
  )

Prototype Set Counts
Number of times prototype set has been included in the study	Number of prototype sets with this count
17	1
19	4
20	3
21	3
22	8
23	10
24	19
25	19
26	20
27	25
28	23
29	24
30	28
31	25
32	17
33	24
34	15
35	13
36	14
37	2
38	3
39	1
40	2
41	1
Note: The number of times a prototype set has been included in the study is equal to the number of participants who rated the set.

# d |> filter(sbjCode==11) |> select(sbjCode,date,trial,pair_label,set,rt,time_elapsed,time)

# d |> group_by(sbjCode,set) |> 
#   summarize (n=n()) |>
#   gt()

#d |> group_by(sbjCode, item_label_1, item_label_2) |> summarise(n=n())

# (1-.33)^8
# (factorial(8)/(factorial(6)*factorial(8-6))) * (.33^6)*((1-.33)^(8-6))
# (factorial(8)/(factorial(7)*factorial(8-7))) *(.33^6)*((1-.33)^(8-7))
# (factorial(8)/(factorial(8)*factorial(8-8))) *(.33^6)*(1-.33)^(8-8)

# d |> pivot_longer(cols=c(item_label_1, item_label_2), names_to="item_label", values_to="item") |> 
#   group_by(sbjCode, item) |> summarise(n=n())

# patternCounts <- d |> pivot_longer(cols=c(item_label_1, item_label_2), names_to="item_label", values_to="item") |> 
#   group_by(item) |> summarise(n=n(),resp=mean(response),sd=sd(response)) |> arrange(desc(n))



# d |> 
#     pivot_longer(cols=c(item_label_1, item_label_2), names_to="item_label", values_to="item") |> select(sbjCode,set,pair_label,item_label,item,response) |>  group_by(set) |>
#     summarize(n=n_distinct(sbjCode)) |> arrange(desc(n)) 




# d |> group_by(sbjCode, file) |> summarise(n=n())
# d |> group_by(sbjCode, set) |> summarise(n=n())

# d |> group_by(sbjCode) |> summarise(n_distinct(file))
# d |> group_by(sbjCode) |> summarise(n_distinct(set))


# sp <- setCounts2 |> 
#   mutate(set=reorder(set,n)) |>
#   ggplot(aes(x=set,y=n)) +
#    geom_col() +
#    theme(legend.title=element_blank(),
#       axis.text.x = element_text(size=5,angle = 90, hjust = 0.5, vjust = 0.5)) +
#     labs(x="Prototype Set", y="Number of Participants to rate set") 

sh <- setCounts2 |> 
  ggplot(aes(x=n)) + geom_histogram(binwidth = 1) +
  scale_x_continuous(breaks=seq(0, max(setCounts2$n), by = 1)) +
  geom_text(stat="count", aes(label=..count..), vjust=-0.5) +
  labs(x="Number of times prototype set has been included in the study", 
  y="Number of prototype sets for each count") 


#sp/sh

sh

Prototype set counts

 

Rating Distributions

pgr <- d |> 
  ggplot(aes(x=response))+geom_histogram(binwidth=1) + 
      scale_x_continuous(breaks=seq(1, 9, by = 1)) +
    coord_cartesian(xlim = c(1, 9)) + labs(title="Aggregate Rating Distribution", x="Rating", y="Count") 

pir <- d |>  ggplot(aes(x=response))+
      geom_histogram(binwidth=1) + 
      facet_wrap(~sbjCode) + 
      scale_x_continuous(breaks=seq(1, 9, by = 1)) +
    coord_cartesian(xlim = c(1, 9)) + labs(title="Rating Distribution per Sbj.", x="Rating", y="Count") 

pgr/pir

Rating distributions

Reaction Time Distributions

prtg <- d |> ggplot(aes(x=rt))+
  geom_density() + 
  labs(title="Aggregate Reaction Time Distribution", x="Reaction Time (ms)", y="Density")

prtid <- d |> ggplot(aes(x=rt))+geom_density() + 
  facet_wrap(~sbjCode,scale="free_x") + labs(title="Reaction Time Distribution per Sbj.", x="Reaction Time (ms)", y="Density")

prtg/prtid

Reaction time distributions

Individual Subject Ratings

# d |> summarize(n=n(), n_distinct(sbjCode), n_distinct(file), n_distinct(set), n_distinct(trial), n_distinct(item_label_1), n_distinct(item_label_2))


# d %>%
#   filter(sbjCode == 11) %>%
#   select(sbjCode, date, trial, set, rt, time) %>%
#   mutate(time_parsed = parse_date_time(paste(date, time), orders = c("mdY IMS p", "mdy IMS p"))) %>%
#   group_by(sbjCode, date) %>%
#   summarise(start_time = min(time_parsed), end_time = max(time_parsed)) %>%
#   mutate(endTimeMinusStart = end_time - start_time)

plot_hist_sbj <- function(id) {
  d |> filter(sbjCode==id) |>
    ggplot(aes(x = response)) +
    geom_histogram(binwidth=1,fill = 'dodgerblue4') +
    scale_x_continuous(breaks=seq(1, 9, by = 1)) +
    coord_cartesian(xlim = c(1, 9)) +
    theme_minimal() +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          axis.text.x=element_text(size=26))  
}


sbj_sum <- d |> group_by(sbjCode) |> 
#filter(sbjCode<5) |>
  mutate(time_parsed = parse_date_time(paste(date, time), orders = c("mdY IMS p", "mdy IMS p"))) |>
  summarize ("Mean Rating"=mean(response),
  "SD Rating"=sd(response), 
  "Mean RT"=mean(rt)/1000, 
  #"Total Time (min)" = max(time_elapsed)/60000,
  "Total Time (min)" = round(difftime(max(time_parsed), min(time_parsed), units = "mins"),1),
  n_prototype_sets = n_distinct(set), 
  "N Trials" = n_distinct(trial)) |> 
  mutate("Response_Distribution"=sbjCode) 

  sbj_sum |> gt() |> 
    text_transform(
    locations = cells_body(columns = 'Response_Distribution'),
    fn = function(column) {
      map(column, plot_hist_sbj) |>
        ggplot_image(height = px(80), aspect_ratio = 3)
    }
    )
sbj_sum |> mutate(total_time=as.numeric(`Total Time (min)`)) |> 
  rename("Subject"=sbjCode, "N_Sets" = n_prototype_sets) |> 
  summarize("Median Completition time (min)"=median(total_time), 
  "Average Completion Time" = mean(total_time), 
  "Min Completion Time (min)" = min(total_time), 
  "Max Completion Time (min)" = max(total_time)) |> kbl()

Table 2: Individual Subject Ratings

sbjCode	Mean Rating	SD Rating	Mean RT	Total Time (min)	n_prototype_sets	N Trials	Response_Distribution
2	6.3	2.0	2.22	14.7	101	303
3	4.7	1.6	1.62	11.6	101	303
4	4.5	1.6	3.52	21.1	101	303
5	4.0	2.0	2.02	13.5	101	303
7	5.1	2.8	2.79	17.4	101	303
8	5.0	2.4	2.10	13.9	101	303
9	4.7	2.1	2.81	17.2	101	303
10	4.6	1.7	2.45	15.8	101	303
11	2.8	1.8	5.80	32.4	101	303
12	4.3	2.6	3.76	22.2	101	303
13	5.8	2.4	1.21	9.5	101	303
14	4.5	1.8	3.74	22	101	303
15	5.1	2.3	2.11	14	101	303
16	6.1	2.1	1.62	11.5	101	303
17	5.5	2.7	1.05	8.7	101	303
18	2.5	2.5	2.13	14.2	101	303
19	4.4	2.3	1.91	13	101	303
20	5.7	1.6	2.18	14.4	101	303
21	4.6	2.3	3.93	23.1	101	303
22	4.4	1.6	3.44	20.6	101	303
23	3.3	2.5	2.00	13.5	101	303
24	4.7	2.3	1.51	11.1	101	303
25	5.2	1.6	3.06	18.8	101	303
26	4.7	2.0	1.83	12.6	101	303
27	4.5	2.6	2.69	17	101	303
28	4.7	2.1	6.25	34.9	101	303
29	4.9	2.2	2.75	17.2	101	303
30	5.3	1.9	0.65	6.6	101	303
31	6.6	2.4	1.14	9	101	303
32	4.0	1.8	6.43	35.7	101	303
33	5.1	3.3	1.40	10.3	101	303
34	5.2	2.5	2.77	17.4	101	303
35	4.7	2.2	3.07	18.9	101	303
36	6.3	2.0	1.46	10.7	101	303
37	5.8	1.9	1.42	10.6	101	303
38	5.8	2.4	1.51	10.8	101	303
39	5.7	1.7	2.61	16.2	101	303
40	4.1	2.0	1.97	13.3	101	303
41	5.1	2.4	3.53	21.1	101	303
42	4.8	1.4	3.57	21.4	101	303
43	5.3	2.0	1.82	12.4	101	303
44	3.3	2.6	2.05	13.7	101	303
45	4.6	1.9	3.31	20.1	101	303
46	5.7	1.6	4.92	28	101	303
48	4.7	2.4	3.26	1328	170	303
49	3.7	2.0	3.07	18.6	101	303
50	4.5	1.8	3.05	18.8	101	303
51	4.8	2.7	4.13	24.1	101	303
52	5.2	2.0	3.45	20.7	101	303
53	6.6	1.9	1.74	12.2	101	303
54	4.7	1.7	1.93	13.1	101	303
55	4.7	2.2	3.59	21.4	101	303
56	5.6	1.5	1.89	12.9	101	303
57	4.7	1.7	5.53	31.3	101	303
58	5.4	2.1	1.90	12.9	101	303
59	4.3	1.7	2.40	15.5	101	303
60	6.0	2.5	0.66	6.8	101	303
61	4.9	1.6	2.25	14.3	101	303
62	5.0	2.4	1.73	12	101	303
63	4.7	2.1	3.77	22.3	101	303
64	4.0	2.0	2.31	15.1	101	303
65	4.0	2.0	2.98	18.1	101	303
66	5.0	3.4	3.10	19	101	303
67	4.5	2.5	2.18	14.3	101	303
68	4.1	2.3	2.52	16.1	101	303
69	4.2	2.2	1.90	13	101	303
70	4.3	2.0	2.46	15.8	101	303
71	5.1	1.9	3.49	21	101	303
72	4.7	2.0	2.12	13.8	101	303
73	5.6	2.4	1.39	10.3	101	303
74	5.5	1.9	2.90	17.8	101	303
75	5.1	1.6	1.67	11.8	101	303
76	4.8	1.8	2.44	15.6	101	303
77	3.9	2.3	2.35	15.2	101	303
78	5.0	2.0	2.61	16.5	101	303
79	2.7	2.0	6.03	33.7	101	303
80	5.3	1.6	1.45	10.7	101	303
81	4.8	1.8	1.52	10.9	101	303
82	5.0	3.1	0.54	6.1	101	303
83	4.3	2.3	2.23	14.6	101	303
84	4.8	2.2	1.60	11.5	101	303
85	4.7	3.3	1.68	11.8	101	303
86	5.1	2.3	2.46	15.8	101	303
87	3.1	2.5	2.83	17.7	101	303
88	4.1	2.7	1.24	9.6	101	303
89	4.4	1.3	2.40	15.4	101	303
90	4.9	2.7	2.79	17.4	101	303

Median Completition time (min)	Average Completion Time	Min Completion Time (min)	Max Completion Time (min)
15	31	6.1	1328

Lowest and Highest Rated Pairs

# patternCounts |> filter(n>=8) |>  slice_min(resp)
# patternCounts |> filter(n>=8) |>  slice_max(resp)

# setCounts |> filter(n>=24) |>  slice_min(resp)
# setCounts |> filter(n>=24) |>  slice_max(resp)


# pairCounts |> filter(n>=5) |>  slice_min(resp,n=2)
# pairCounts |> filter(n>=5) |>  slice_max(resp)

min_resp=7
n_show=3

d %>% filter(pair_label %in% {pairCounts |> filter(n>=min_resp) |>  
  slice_min(mean_resp,n=n_show, with_ties=FALSE) |> pull(pair_label)} ) |>
  group_by(pair_label) |>
  slice_head(n=1) %>%
  plot_dotsAll() + 
  plot_annotation(title=glue::glue("Lowest rated pairs ( out of sets with n>={min_resp} ratings)"), theme = theme(plot.title = element_text(hjust = 0.4)))

d %>% filter(pair_label %in% {pairCounts |> filter(n>=min_resp) |>  
  slice_max(mean_resp,n=n_show, with_ties=FALSE) |> pull(pair_label)} ) |>
  group_by(pair_label) |>
  slice_head(n=1) %>%
  plot_dotsAll() +  
  plot_annotation(title=glue::glue("Highest rated pairs ( out of sets with n>={min_resp} ratings)"), theme = theme(plot.title = element_text(hjust = 0.4)))

Pattern Pair Table

All pairs with >=25 ratings

• click on column headers to change sort order
  • e.g. clicking on “Mean Rating” will toggle showing the pairs rated most similar or most dissimilar
  • clicking on “SD” will toggle showing the pairs with the most or least agreement in ratings
• note your screen may need to be at full width to see all columns

plot_hist_pair <- function(Pair) {
  d |> filter(pair_label==Pair) |>
    ggplot(aes(x = response)) +
    geom_histogram(binwidth=1,fill = 'dodgerblue4') +
    scale_x_continuous(breaks=seq(1, 9, by = 1)) +
    coord_cartesian(xlim = c(1, 9)) +
    theme_minimal() +
    theme(axis.title.x=element_blank(),
          axis.title.y=element_blank(),
          axis.text.x=element_text(size=26))  
}



pat_table_plot <- function(Pair){

  df <- d |> filter(pair_label==Pair) |> slice_head(n=1) 

    pat1 <- df %>%
          mutate(pattern_1 = purrr::map(pattern_1, jsonlite::fromJSON)) %>%
          unnest(pattern_1) %>%
          mutate(y=-y, pat=item_label_1) |> select(pair_label,x,y,pat)

    pat2 <- df %>%
          mutate(pattern_2 = purrr::map(pattern_2, jsonlite::fromJSON)) %>%
          unnest(pattern_2) %>%
          mutate(y=-y, pat=item_label_2) |> select(pair_label,x,y,pat)

    pat <- rbind(pat1,pat2)

     pat |> 
    ggplot(aes(x = x, y = y,fill=pat,col=pat)) +
          geom_point(alpha=2) +
          coord_cartesian(xlim = c(-25, 35.5), ylim = c(-25, 25)) +
          theme_minimal() +
          facet_wrap2(~pat,ncol=2,axes="all") + 
          #theme_blank +
          theme_void() + 
          theme(strip.text = element_text(size = 7,hjust=.5),
                panel.spacing.x=unit(-7.3, "lines"), 
                #strip.background = element_rect(colour = "black", linewidth = 2),
                legend.position = "none",
        axis.line.y = element_line(colour = "black", linewidth = .1)) 
}



p5 <- pairCounts |> filter(n>=34) 

p5 |> 
arrange(mean_resp) |>
relocate(pair_label,.after=sd) |>
rename("Pair"=pair_label, "N"=n, "Mean Rating"=mean_resp, "SD"=sd) |>
mutate(Rating_Dist=Pair) |> 
#group_by(Pair) |> 
gt() |> 
tab_options(table.font.size = px(8L)) |>
  cols_width(
    set ~ px(116),
    Pair ~ px(415),
    `N` ~ px(50),
    `Mean Rating` ~ px(90),
    SD ~ px(55)
  ) |>  
  fmt_number(decimals = 1) |> #fmt_integer() |>
  cols_align('left', columns = set) |> 
  text_transform(
    locations = cells_body(columns = Pair),
    fn = function(column) {
      map(column, pat_table_plot) |>
        ggplot_image(height = px(230), aspect_ratio = 1.8)
    }
  ) |>
  text_transform(
    locations = cells_body(columns = Rating_Dist),
    fn = function(column) {
      map(column, plot_hist_pair) |>
        ggplot_image(height = px(150), aspect_ratio = 1)
    }
  ) |>  
   opt_interactive(page_size_default=5, 
     use_page_size_select= TRUE, use_search=TRUE, use_resizers=TRUE,use_filters=TRUE, page_size_values = c(5, 10, 25, 50, 100)) 

# #| fig-cap: dot plots
# #| fig-width: 10
# #| fig-height: 12

# d %>% filter(trial==1) %>%
#   plot_dots()

# d %>% filter(trial==1) %>%
#   plot_dots2()

# d %>% filter(trial<2) %>%
#   plot_dotsAll()

#| fig-width: 6
#| fig-height: 9


# d %>% filter(file %in% unique(d$file[1])) %>%
#   plot_dotsAll()

# plot_dotsAll_orig <- function(df) {
#   plots <- list()

#   for (i in 1:nrow(df)) {
#     p1 <- df[i, ] %>%
#       pivot_longer(cols = starts_with("x"), names_to = "dot", values_to = "x") %>%
#       mutate(dot = as.numeric(str_remove(dot, "x"))) %>%
#       pivot_longer(cols = starts_with("y"), names_to = "dot2", values_to = "y") %>%
#       mutate(dot2 = as.numeric(str_remove(dot2, "y"))) %>%
#       filter(dot == dot2) %>%
#       ggplot(aes(x = x, y = y)) +
#       geom_point() +
#       coord_cartesian(xlim = c(-25, 25), ylim = c(-25, 25)) +
#       theme_minimal() +
#       labs(title = df$id[i]) + theme_blank

#     plots <- append(plots, list(p1))
#   }

#   patchwork::wrap_plots(plots, ncol = 1)
# }

# mc24_proto |> filter(file %in% unique(d$file[1])) %>% plot_dotsAll_orig()

Stucture of dataframe after merging similarity ratings with CatLearn accuracy

• Each subject in the 2024 study has a similarity score for each of their 3 categories. (averaged over 2 comparisons with that categories prototype)
• The same category similarity scores are then compared to their accuracy for each of the 5 Pattern Tyeps (old, prototype, new low, new med, new high)
• 5 Pattern types * 3 Categories = 15 comparisons per subject

Table 3: Example of how data is structure after combining similarity ratings with CatLearn accuracy. 5 Pattern types * 3 Categories = 15 rows per subject in the 2024 study

sbjCode	condit	Category	Pattern.Type	n	CatLearn Accuracy	n_rating	Category Similarity	sd	item
316	high	1	prototype	1	1	60	5.1	2.6	316_high_1_285
316	high	1	old	9	0.4	60	5.1	2.6	316_high_1_285
316	high	1	new_low	3	0.3	60	5.1	2.6	316_high_1_285
316	high	1	new_med	6	0.3	60	5.1	2.6	316_high_1_285
316	high	1	new_high	9	0.4	60	5.1	2.6	316_high_1_285
316	high	2	prototype	1	0	60	3.8	2	316_high_2_327
316	high	2	old	9	0.7	60	3.8	2	316_high_2_327
316	high	2	new_low	3	0.7	60	3.8	2	316_high_2_327
316	high	2	new_med	6	0.2	60	3.8	2	316_high_2_327
316	high	2	new_high	9	0.2	60	3.8	2	316_high_2_327
316	high	3	prototype	1	0	60	5.2	2.4	316_high_3_287
316	high	3	old	9	0.1	60	5.2	2.4	316_high_3_287
316	high	3	new_low	3	0.7	60	5.2	2.4	316_high_3_287
316	high	3	new_med	6	0	60	5.2	2.4	316_high_3_287
316	high	3	new_high	9	0.3	60	5.2	2.4	316_high_3_287

cat_sim_test %>% # round all numerics except sbjCode to 2 decimal places
 mutate(across(where(is.numeric), ~round(., 1))) |> select(-id,-sim_group,-item_label) |> 
  relocate(item,file, .after=sd) |>
  select(-Phase,-Block) |> 
  rename("Category Similarity" = resp, "CatLearn Accuracy" = Corr) |>
   DT::datatable(options = list(pageLength = 6))
# cat_sim_test %>% # round all numerics except sbjCode to 2 decimal places
#      mutate(across(where(is.numeric), ~round(., 1))) |> select(-id,-sim_group,-item_label) |> 
#      filter(sbjCode==316) |> 
#       relocate(item,file, .after=sd) |>
#       select(-Phase,-Block, -file) |> 
#       rename("Category Similarity" = resp, "CatLearn Accuracy" = Corr) |> pander::pandoc.table(style="rmarkdown",split.table=Inf)

Table 4: Example of how data is structure after combining similarity ratings with CatLearn accuracy. 5 Pattern types * 3 Categories = 15 rows per subject in the 2024 study